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code/paddle-ocr-demo/test.py

@@ -1,74 +0,0 @@
-from paddleocr import PPChatOCRv4Doc
-import paddle
-# 检查 PaddlePaddle 是否安装成功
-# 打印是否支持 GPU
-print("是否支持 GPU:", paddle.is_compiled_with_cuda())
-
-# 如果支持，再看看当前是否在使用 GPU
-if paddle.is_compiled_with_cuda():
-    print("当前是否使用 GPU:", paddle.device.get_device())
-
-chat_bot_config = {
-    "module_name": "chat_bot",
-    "model_name": "ernie-3.5-8k",
-    "base_url": "https://qianfan.baidubce.com/v2",
-    "api_type": "openai",
-    "api_key": "bce-v3/ALTAK-5bKjjmEZxz7bnIjpWxxZT/723849a3548dc0d9f639b0ffaed5255a1ccf3f30",  # your api_key
-}
-
-retriever_config = {
-    "module_name": "retriever",
-    "model_name": "embedding-v1",
-    "base_url": "https://qianfan.baidubce.com/v2",
-    "api_type": "qianfan",
-    "api_key": "bce-v3/ALTAK-5bKjjmEZxz7bnIjpWxxZT/723849a3548dc0d9f639b0ffaed5255a1ccf3f30",  # your api_key
-}
-
-pipeline = PPChatOCRv4Doc(
-    use_doc_orientation_classify=False,
-    use_doc_unwarping=False
-)
-
-visual_predict_res = pipeline.visual_predict(
-    input="https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_certificate-1.png",
-    use_common_ocr=True,
-    use_seal_recognition=True,
-    use_table_recognition=True,
-)
-
-mllm_predict_info = None
-use_mllm = False
-# 如果使用多模态大模型，需要启动本地 mllm 服务，可以参考文档：https://github.com/PaddlePaddle/PaddleX/blob/release/3.0/docs/pipeline_usage/tutorials/vlm_pipelines/doc_understanding.md 进行部署，并更新 mllm_chat_bot_config 配置。
-if use_mllm:
-    mllm_chat_bot_config = {
-        "module_name": "chat_bot",
-        "model_name": "PP-DocBee",
-        "base_url": "http://127.0.0.1:8080/",  # your local mllm service url
-        "api_type": "openai",
-        "api_key": "api_key",  # your api_key
-    }
-
-    mllm_predict_res = pipeline.mllm_pred(
-        input="test1.png",
-        key_list=["Shipper"],
-        mllm_chat_bot_config=mllm_chat_bot_config,
-    )
-    mllm_predict_info = mllm_predict_res["mllm_res"]
-
-visual_info_list = []
-for res in visual_predict_res:
-    visual_info_list.append(res["visual_info"])
-    layout_parsing_result = res["layout_parsing_result"]
-
-vector_info = pipeline.build_vector(
-    visual_info_list, flag_save_bytes_vector=True, retriever_config=retriever_config
-)
-chat_result = pipeline.chat(
-    key_list=["Shipper"],
-    visual_info=visual_info_list,
-    vector_info=vector_info,
-    mllm_predict_info=mllm_predict_info,
-    chat_bot_config=chat_bot_config,
-    retriever_config=retriever_config,
-)
-print(chat_result)